Effective SAR Image Segmentation and Sea-Ice Floe Distribution

Analysis via Kernel Graph Cuts based Feature Extraction and Fusion

Soumitra Sakhalkar

, Jinchang Ren

, Phil Hwang

and Paul Murray

Centre for Excellence in Signal & Image Processing (CeSIP), Department of Electronic and Electrical Engineering,

University of Strathclyde, Glasgow, G1 1XQ, U.K.

Scottish Association for Marine Science (SAMS), Oban, PA37 1QA, U.K.

1 RESEARCH PROBLEM

The Sea Ice that grows in the open seas like the

Arctic sea, forms varying shapes and size due to the

fracturing as well as thickening caused by the strong

gale force winds and sea waves. Over the winter

season, due to the cooler temperature, these sea-ice

regions combine with each other to make a stronger

and larger sea ice block. In the summer however,

due to the higher temperature, they separate into

smaller and weaker floes as shown in Figure 1.

Figure 1: An area of sea ice region captured during the

beginning of the month of the early summer period on the

left and during the ending in the month on the right.

Sea-Ice monitoring has gained significant interest in

recent years, largely due to the fact of the decreasing

area and thickness of the older arctic sea ice (Kwok,

et al., 2009) (Stroeve, et al., 2008). This decline in

older sea ice has been linked largely to the growth of

younger, thinner sea ice regions (Maslanik, et al.,

2007) and also climate changes (Holloway & Sou,

2002), caused by greenhouse gases (Serreze, et al.,

2007).

The study of Polar Regions using Synthetic

Aperture Radar [SAR] has been widely used for

identification of sea ice floes, their size and their

distribution (Burns, et al., 1987) (Rothrock &

Thorndike, 1984), (Soh, et al., 2004), (Soh &

Tsatsoulis, 1998). This is because SAR is not

majorly affected by the harsh weather conditions or

the illumination variations and it is able to cover

large and primarly inaccesible areas (Xu, et al.,

2014). This is particularly important for ensuring

safe marine navigation as well as supporting studies

of climate changes, like ours, of the Polar Regions.

To date, the process of developing an automatic

algorithm for effective segmentation of SAR Sea-Ice

images has not been achievable. As a result, analysis

of sea ice images relies on a time consuming expert

analysis which is performed manually. For this

reason, it is primarily important to develop

techniques to automatically segment the sea-ice

regions from the background and subsequently

extract these sea-ice regions from the SAR image.

When this is completed it will become possible to

build a Floe Size Distribution (FSD) database, where

FSD is a measure of the distribution of the different

size of the sea ice floes. An FSD database will be

constructed in our project by extracting and storing

the total pixel area of these individual sea-ice

regions in the SAR image and grouping them

according to their size. The result will then be used

to generate a graph of the size distribution of the

floes on different days in a year of the Arctic region.

The outcome of our study will further develop

scientist’s understanding of the different trends as

well as the various conditions affecting the size of

the Arctic sea ice floes for that particular year.

Eventually this will improve our understanding of

the changes in the sea-ice extent over the year by

means of comparison with the past several years’

results.

The key research problem addressed by this

work lies in developing a new novel image

segmentation technique which is simple, fast and

robust when used to segment the SAR sea ice

images.

Sakhalkar S., Ren J., Hwang P. and Murray P..

Effective SAR Image Segmentation and Sea-Ice Floe Distribution Anlysis via Kernel Graph Cuts based Feature Extraction and Fusion.

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

2 OUTLINE OF OBJECTIVES

The main aim of this study is to create novel

techniques to automatically segment and extract the

sea ice floes from the SAR images of the area in the

Arctic region being monitored. To achieve this it is

important to fulfil the following smaller objectives;

1. To develop an optimal segmentation technique

for accurately segmenting individual ice floes

from the background as well as from each

other.

2. To refine the methods developed in 1 so they

are efficient and inexpensive to compute.

3. To remove/reduce the speckle noise present in

almost every SAR image using appropriate

filters. That is, using filters which retain the

original image characteristics as well as

removing/reducing the presence of speckle

noise.

4. To make the techniques developed completely

automatic and dynamic so that they can

process any SAR image to segment the ice

floes.

The result of completing these objectives will be

a robust and efficient global machine learning

algorithm for segmentation. Furthermore the

algorithm parameters will be computed

automatically depending on the image statistics

derived from the current image under study. This

will introduce a step change in the way the sea ice

floes are analysed.

3 STATE OF THE ART

Due to the decrease in sea ice floe extent and

thickness, it has become increasingly important to

generate a better understanding of the environmental

as well as the social impacts on the transformation of

Arctic sea ice over the duration of each day in a

year.

The need for automated segmentation techniques

for ice floe analysis has led to some popular floe

related studies: examples include techniques for

classification using dynamic thresholding for

separation and heuristic geophysical knowledge

(Haverkamp, et al., 1995), deformation of sea ice by

means of measuring the opening and closing of leads

(Fily & Rothrock, 1990) and floe size identifications

and measurement (Korsnes, 1993). More recent

studies have tried to achieve this by using the two

texture analysis methods, Markov Random Fields

(MRF) and Gray-Level Co-occurrence Probabilities

(GLCP) (Clausi & Yue, 2004), or by using

stochastic ensemble consensus approach (Wong, et

al., 2009), utilisation of Bayesian segmentation

approach with MRF model (Deng & Clausi, 2005)

and using pulse-coupled neural networks (PCNN)

(Karvonen, 2004).

Although many of these approaches have been

implemented, tested and provided in the literature,

they do not meet the necessary criteria for

application to our data. This is largely due to the fact

that the existing techniques are either data specific

and can only be applied to segment sea ice floes in

certain parts of the world or they involve

manipulation of the data or in other cases take too

long to compute. As a result, the currently used

approach relies on the lengthy technique of manually

segmenting the regions based on expertise and

knowledge of a sea-ice expert.

There have also been some automated

approaches for Sea-Ice segmentation; using an

intelligent system named Advanced Reasoning using

Knowledge for Typing Of Sea ice (ARKTOS).

ARKTOS automatically segments the regions and

generate the descriptors for the segmentation of sea

ice floes. Others use expert rules for classification

(Soh, et al., 2004) using the analytical tool:

Automated sea ice segmentation (ASIS). ASIS uses

local thresholding for obtaining and retaining

information in the image. Image quantization has

also been used to obtain the different classes within

an image and computing spatial attributes of each

class using Aggregated Population Equalization

(APE) concept (Soh & Tsatsoulis, 1998).

Nonetheless all these have been deemed

unsatisfactory to be used for our study due to the

same reasons indicated before.

Recent developments in image segmentation

techniques have led to a new approach called Graph

Cuts [GC] with kernel mapping which is based on

the work of (Salah, et al., 2011). GC is based on

energy minimization for effectively finding a cut

{segmentation} between regions (Boykov, et al.,

2001). GC have been used in many studies for image

segmentation, for instance in (Rother, et al., 2004),

an iterative and interactive GC based approach has

been used for effective foreground extraction,

whereas in (Boykov & Funka-Lea, 2006) &

(Boykov, et al., 2001) GC is used for segmenting the

regions using image histogram analysis.

In fact many other approaches based on energy

minimization, similar to GC have also been used for

image segmentation; Region Competition (Zhu &

Yuille, 1996), Active Contours (Caselles, et al.,

1997) (Chan & Vese, 2001) and Level Set using

EffectiveSARImageSegmentationandSea-IceFloeDistributionAnlysisviaKernelGraphCutsbasedFeatureExtraction

andFusion

Figure 2: Methodology of our study.

Mumford and Shah model (Vese & Chan, 2002).

Also in many studies like (Muller, et al., 2001),

(Scholkopf, et al., 1999), (Dhillon, et al., 2007),

(Schölkopf, et al., 1998), (Girolami, 2002) and

(Zhang & Chen, 2002), the so called kernel trick has

been used for effective and efficient clustering of

complex data.

Similarly many studies have used Kernel mapped

Graph cuts (KGC) for image segmentation,

examples include spine image fusion to replace CT

and MR (Miles, et al., 2013), for classification of

brain images (Harini & Chandrasekar, 2012), for

segmentation of abdomen MR images (Luo, et al.,

2013), for segmentation for MR images with

intensity inhomogeneity correction (Luo, et al.,

2013).

Thus it can be seen how Kernel mapped Graph

Cuts, have until now, been mostly used for medical

image processing as opposed to SAR sea ice image

processing like our study. In fact, to the author’s

knowledge KGC based techniques have not yet been

used to segment images containing ice floes. It is

therefore proposed that a technique using GC will be

applied to automatically segment ice floes in SAR

imagery. To deal with the speckle noise, we also

propose the addition of an effective pre-processing

and image filtering stage which will lead to an

optimal segmentation result.

4 METHODOLOGY

The entire processing procedure for our study is

shown by the flow chart given in the Figure 2. As

seen in Figure 2, the methodology for our study is

split in two major stages;

1. This will involve segmentation of sea ice floe

using the existing KGC algorithm. It will also

incorporate our proposed contribution of the

addition of a pre-processing stage to improve

the results and a technique to allow the

optimization of the parameters which will

make the algorithm adapt automatically for

processing the image under study.

2. On completion of Stage 1, the next step will

involve the extraction of individual sea ice

floes to build the FSD analysis for each image

for each day in that particular year of study.

We will now briefly explain the existing

algorithm; KGC and then explain the importance of

our contribution to the algorithm for the

improvement of results.

4.1 The Kernel Graph Cuts

Our implementation of the existing KGC algorithm

is based on the implementation by Salah, et. al.

(Salah, et al., 2011), who initially proposed this

technique. This algorithm is based on a three stage

processing procedure;

1. K-means clustering to find the initial clusters

and their centroids.

2. Kernel mapping of the image into higher

dimensional feature space.

3. Image Segmentation achieved using Graph

Cuts.

SENSORNETS2015-DoctoralConsortium

4.1.1 K-means based Clustering

K-means is a popular un-supervised and easy to

implement clustering method, first introduced by

Lloyd (Lloyd, 1982). In fact a survey of clustering

algorithm some years ago showed how K-means,

even after 25 years was still the most widely used

clustering algorithm (Berkhin, 2002) at that time.

The algorithm partitions/clusters a given set of

data into k clusters depending on the least squared

distance of each point from that cluster’s centroid.

K-means is an iterative process, which continually

estimates the least squared distance from the cluster

centroid and re-assigns the data into these ‘k’

clusters until the process is stabilized. An example

of a set of data points clustered into 3 clusters

represented by the three different colours for each

one, is illustrated in Figure 3.

Figure 3: K-means based clustering example.

4.1.2 Kernel Mapping

Kernel mapping or the kernel trick is a popular

technique used in many recent image segmentation

algorithms (Muller, et al., 2001), (Scholkopf, et al.,

1999), (Dhillon, et al., 2007), (Schölkopf, et al.,

1998), (Girolami, 2002) , where a kernel function is

used to map a data set into a higher dimensional

feature space, so that partition of regions is possible.

Figure 4, first seen in (Salah, et al., 2011),

illustrates how the kernel mapping aids a better and

faster separation /segmentation of result, due to the

implicit mapping of data set into higher dimensional

space, so that the GC algorithm can be applied.

There are many popular kernel functions

commonly used in the field of digital image

processing; examples include the Gaussian/ radial

basis function kernel, polynomial kernel, sigmoid

kernel and many more as mentioned in (Genton,

2002).

Figure 4: Illustration of a non-linear data separation. Data

separation is non-linear in data space. The data is mapped

into a higher dimensional feature space using a kernel. The

separation is now linear in feature space, separated by a

hyper plane.

For our study, we will use the Gaussian/ radial basis

function (RBF) (Buhmann, 2003) kernel, due to its

simplicity and ease of implementation, for mapping

into higher dimensional feature space. The equation

for the RBF kernel is given by,





,









‖

–

‖











(1)

4.1.3 Graph Cut based Image Segmentation

The implementation of the algorithm of KGC as first

proposed by Salah (Salah, et al., 2011), is based on

the GC technique first introduced in (Boykov, et al.,

2001), which implements the energy minimization

based on the minimization of the two energy terms

given by,





E









E









(2)

Here, E

Smooth

is the Smoothness cost which measures

the extent to which a label f is no longer piecewise

constant and E

Data

is the Data cost which measures

the disagreement of the current labelling f with the

observed data. The labelling f mentioned here is

assigned by the K-means clustering algorithm in the

initial stage of the KGC algorithm. A labelling f is

said to be piecewise constant if it varies smoothly on

the surface of the object but changes dramatically at

the object boundaries.

Figure 7 shows the step-by-step procedure of

how a GC algorithm finds a cut {segmentation}.

Figure 7(a) shows the initial labelling for the pixels

before loading the graph for the GC algorithm; blue

EffectiveSARImageSegmentationandSea-IceFloeDistributionAnlysisviaKernelGraphCutsbasedFeatureExtraction

andFusion

pixels (p - s) belong to label A and red pixels (t - x)

belong to label B. In Figure 7(b) the GC algorithm

then assigns the labelling and weights of each pixel

with each of the labels A and B using the Data cost

term. The darker arrows denote the more likelihood

of a label being assigned to a pixel, while the dotted

arrow denotes low likelihood of a label being

assigned to a pixel. It can be seen how the pixel u is

now indicated to be more likely to present label A. In

Figure 7(c) the weights of each pixel with its

neighbouring pixel are then assigned using the

Smoothness cost term. Similar to the previous

section, the darker lines denote the more likelihood

and the weaker lines show less likelihood of a pixel

being associated to be similar to each other. In

Figure 7(d) the GC algorithm finds a cut between the

labelling one neighbouring pixel pair at a time. It

can be seen how the GC now assigns pixel u to label

A based on the most likelihood (Data Cost) and

similarity measure (Smoothness Cost).

4.1.4 Drawbacks

Although the KGC algorithm has various advantages

over the conventional image segmentation

techniques, it still has limited number of drawbacks

which need to be addressed to make the image

segmentation more robust and efficient.

Figure 5 shows an area where the KGC works

really well and produces really good results and in

Figure 6, it can be seen how the KGC produces very

poor results due to the heavy presence of speckle

noise in that region of the original SAR image.

Figure 5: One example result of the KGC segmentation.

4.2 Pre Processing

For our study, we propose the addition of a two

stage pre-processing routine to help improve the

Figure 6: Other example result of the KGC segmentation.

performance of the existing KGC based

segmentation algorithm. This pre-processing step

involves filtering the image using adaptive filters to

remove the speckle noise before applying

morphological processing. The aim is to improve the

classification result from the K-means and

subsequently the segmentation result from the KGC

algorithm by first applying these pre-processing

techniques.

4.2.1 Adaptive Filtering

Most SAR images contain the multiplicative noise

known as speckle. This presence of speckle noise

reduces the detection of targets or patterns present in

the SAR sea ice images (Sheng & Xia, 1996).

Hence for this purpose we have used the

Adaptive Median (AM) filter (Qiu, et al., 2004),

which uses the local statistics within a filter window

to mark a pixel as speckle noise and remove/reduce

this speckle noise present in the image. We have

compared our results with other popular speckle

filtering techniques like the Lee filter (Lee, 1980)

(Lee, 1981), Frost filter (Frost, et al., 1982) (Frost, et

al., 1981), Bilateral filter (Tomasi, 1998), Median

filter & Wiener filter (Lim, 1990), Local Sigma filter

(Eliason & McEwen, 1990) and found that the AM

filter to be the most suitable for our study. For the

scope of this paper, we have not added the

comparison results.

4.2.2 Morphological Processing

Other popular techniques, widely used for pre and

post processing are the morphological filters such as

dilation and erosion (Matheron, 1975) (Serra, 1982)

(Dougherty & Lotufo, 2003). In terms of image

processing, Dilation, enlarges the image features

SENSORNETS2015-DoctoralConsortium

Figure 7: Step-by-Step procedure of GC algorithm finding a cut using min cut-max flow algorithm.

Figure 8: Morphological processing on the KGC segmentation result.

based upon the size and shape of the structuring

element chosen. Erosion shrinks the image features

and can also remove them based upon the size and

shape of the structuring element chosen. The

morphological processing is extensively explained

extensively in (Gonzalez, et al., 2004). For the

purpose of our study we have used morphological

closing, which is a dilation followed by an erosion

using the same structuring element.

For our study, we have used morphological

processing to overcome the aforementioned

drawbacks of the KGC algorithm and produce better

K-means clustering results.

For this purpose, we build a mask image, by

thresholding the original grayscale image using

standard deviation. We use standard deviation rather

than grey level threshold for thresholding to make

the process more adaptive to the current image under

study.

This is then followed by our addition of

EffectiveSARImageSegmentationandSea-IceFloeDistributionAnlysisviaKernelGraphCutsbasedFeatureExtraction

andFusion

morphological processing. We then multiply this

mask image with the original grayscale image to

produce a morphologically enhanced image as seen

in Figure 8.

We have used the disk shaped structuring

element for our work, since the shape almost

represents the shape of an ideal sea ice floe. Figure

8, shows an example result achieved using the disk

structuring element.

5 EXPECTED OUTCOME

It is anticipated that a major outcome of this study

will be a novel, fast and reliable algorithm for sea

ice floe image segmentation. The algorithm will be

easy to use so that environmental experts are able to

replace the current manual analysis with this

sophisticated and robust technique.

Beyond this, the outcome of this work will help

us develop our understanding of the environmental

as well as social factors affecting the Arctic Sea ice

floe cover. For this, a detailed analysis of sea ice

floe needs to be done, with the implementation of

the FSD analysis. This will be done to monitor the

sea ice floe extent on each day of subsequent year.

This data will then be compared with the results

of the FSD analysis done in the same area in the

Arctic Region from the previous years and will then

help us validate the theory that the older Arctic sea

ice floes are indeed getting reduced and replaced by

younger, weaker sea ice floes.

Beyond this it is also anticipated that this study

can be applied to a wide range of applications. Some

examples would include segmenting medical images

of a biological nature or microscopic images of

metals and other similar materials. Our addition of

the adaptive filtering process can also be used for

other noise removal applications.

6 STAGE OF THE RESEARCH

6.1 Results

We now present our results by means of a

comparison between the results produced with the

original KGC algorithm and the results produced as

a result of our addition of the proposed pre-

processing stage. To validate the efficacy of the

proposed approach, real SAR images with a high

resolution of 16k by 16k have been used for both

visual assessment and quantitative analysis.

The algorithm is coded in Matlab running on a

Dell Inspiron 5537 laptop with 2.3 GHz processor, 4

GB RAM and 64 bit Windows 8.1 operating system.

It requires approximately 41 minutes for obtaining

Figure 9: Comparison Result on Image 1.

SENSORNETS2015-DoctoralConsortium

Figure 10: Comparison Result on Image 2.

the segmentation result for the entire 16k by 16k

original SAR image. The processing speed can be

further optimised and reduced to run even faster, but

it is currently not the main area of focus of our

study.

In Figures 9 and 10, the images on the left are the

original real SAR sea ice images, the images in the

middle are the Original KGC segmented results and

the images on the right are the results produced after

our addition of a pre-processing stage. It can be seen

how our addition improves the segmentation result

of the five sample sub sections of the main 16k by

16k real SAR Sea-Ice image, visually.

6.2 Progress to Date

We have now met the first 3 objectives of our study.

These involved selecting a fast, accurate and

adaptive algorithm for segmenting the sea ice floes

and refining the algorithm to make it more efficient.

These can be verified from the descriptions of

the KGC algorithm in the previous sections and as

evidently seen in Figures 9 & 10. Figures 9 & 10

portray how our proposed pre-processing stage,

removes/reduces the speckle noise present in the

SAR images and validates how our addition

improves the KGC segmented results for the SAR

sea ice images.

6.3 Future Work

We now need to focus on extracting the floe size

information of these segmented sea ice floes. In

order to achieve this, it is first necessary to further

separate the floe regions which are not currently

separated in some regions but which can be visually

predicted to be separated. For achieving this, we are

currently implementing another popular energy

based image segmentation algorithm; Active

Contours (Caselles, et al., 1997) or also referred to

as “snakes”. Active Contours currently being used

for our study are based on the algorithm developed

by Chan & Vese (Chan & Vese, 2001).

The active contour is although known to be

notoriously slow due to the large number of

iterations required to achieve a good segmentation.

To reduce this processing time, we are currently also

building an adaptive algorithm to only extract the

regions where separations of sea ice floe need to be

implemented as per our visual perception. We have

been able to achieve some minor improvements but

more work needs to be done in order to achieve the

optimal results for the extraction of these sea ice floe

regions.

EffectiveSARImageSegmentationandSea-IceFloeDistributionAnlysisviaKernelGraphCutsbasedFeatureExtraction

andFusion

ACKNOWLEDGEMENTS

We would like to thank Scottish Association for

Marine Science (SAMS) and NERC for providing us

with such a challenging and interesting topic for our

study and for their funding support "NE/L012707/1"

and "NE/M00600x/1" to make this study possible.

We would also like to thank the University of

Strathclyde for their motivation and support for

conducting this study.

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EffectiveSARImageSegmentationandSea-IceFloeDistributionAnlysisviaKernelGraphCutsbasedFeatureExtraction

andFusion